Weights and biases are trained in a stochastic gradient approach via backpropagation. In this work the architecture is kept very small due to the limited number of samples: The structure is varied between two to four layer depth with a width of ten neurons. We test both the relu and sigmoid activation functions. Training is done with the Adam stochastic optimizer [1]}. For more details on KRR, the interested reader is referred to the book of Bishop [2]}, whereas for the other methods we refer to the standard textbook of Hastie [3]}. For KRR and the decision tree, the scikit-learn package was used [4]} while for the multilayer perceptron the Keras library was used with Tensorflow as backend [5]}, [6]}.
